Activity recognition has been a challenging topic in computer vision. The range of activities to be recognized may vary broadly in terms of their specific usage such as in video search, video surveillance, and human-robot interaction. Among others, computer vision technology for real-time video surveillance has achieved fast development in recent years. Traditionally, recognition algorithms are executed in local devices. However, as algorithms become more and more complex and “Cloud” computation becomes widely adopted nowadays, many complex recognition algorithms are executed in a “Cloud” server.
Complex recognition algorithm executed in a Cloud server may recognize more human activities, which improves recognition accuracy. However, Cloud computation may involve problems concerning network flow and delay, which is difficult to solve, especially when high-definition cameras become prevalent and the data volume uploaded to the Cloud servers increases significantly. Traditional hardware companies, such as those manufacturing cameras and surveillance devices, and traditional algorithm providers do not have access to each other's resources, and therefore fail to streamline activity recognition.